Job Description
We are looking to onboard a Computer Vision Optimization Engineer to strengthen our Computer Vision function within Machine Learning. This is a mid level opening based out of Coimbatore, offered on a full-time basis.
The ideal candidate holds B.Tech (CS/AI) and brings roughly 3 years of hands-on exposure to computer vision. Day-to-day, the work draws heavily on Deep Learning, Computer Vision, Python, Model Evaluation & Tuning, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 24.4-40.7 LPA, in line with market standards for mid level roles within machine learning.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Mornings are usually spent on planning and status reviews, while the rest of the day is given over to hands-on delivery and problem-solving. References may be requested closer to the offer stage rather than at the start of the process.
Compensation includes standard statutory benefits along with performance-linked variable pay. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
High performers in this role typically move into a broader ownership scope within 18-24 months. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.
The ideal candidate holds B.Tech (CS/AI) and brings roughly 3 years of hands-on exposure to computer vision. Day-to-day, the work draws heavily on Deep Learning, Computer Vision, Python, Model Evaluation & Tuning, and the person will be expected to apply these skills independently rather than under constant supervision.
Compensation for this position is structured around INR 24.4-40.7 LPA, in line with market standards for mid level roles within machine learning.
A typical day starts with reviewing priority tasks and syncing with the immediate team before moving into focused execution work. The role alternates between independent deep work and short coordination huddles with stakeholders across functions.
No two days look identical - some are heavy on execution, others are spent largely in planning and cross-team discussions. Mornings are usually spent on planning and status reviews, while the rest of the day is given over to hands-on delivery and problem-solving. References may be requested closer to the offer stage rather than at the start of the process.
Compensation includes standard statutory benefits along with performance-linked variable pay. There's usually a block of uninterrupted time set aside for focused work, separate from meetings and coordination calls.
High performers in this role typically move into a broader ownership scope within 18-24 months. Priorities can shift mid-week depending on business needs, so adaptability matters as much as technical depth.
Applications without a clear summary of relevant experience are less likely to be shortlisted for the first round. New joiners typically get a short structured onboarding before being handed independent ownership of their first deliverables. Shortlisted applicants will be evaluated on both domain knowledge and practical problem-solving ability.
This is a good fit for someone who wants real ownership rather than a purely execution-only role.